Agricultural Finance & Risk
Shauna Phillips
School of Economics
Copyright By PowCoder代写 加微信 powcoder
AREC3005 Agricultural Finance & Risk STAFF
Dr Shauna Phillips (Unit Coordinator) Phone: 93517892
R549 Social Sciences Building
Cattle and smoke near the Sir Ivan fire. Photo:
Lectures and tutorials
› Lectures: If you are reading this right now, you’re probably in the right place….
› Time: Tuesday 9am 3hrs (includes tutorial) › Venue: Old Teachers College LT 306
› Consultation:
– Flexible consultation times via Zoom, but emails are essential for all appointments
Learning outcomes
› LO1. demonstrate a sound understanding of theoretical knowledge of decision making under risk and uncertainty in agriculture
› LO2. demonstrate a sound understanding of the methods and tools for dealing with risk and uncertainty that are available to agricultural producers
› LO3. demonstrate a sound understanding of different representations of risk and uncertainty
› LO4. demonstrate an ability to use data to calculate different measures of risk and uncertainty
› LO5. demonstrate an ability to clearly communicate results and implications of applied economic analysis.
Assessment
› 20% Mid-semester exam (60 mins), in Week 8
› 12April 10am
› Short answer questions – examples will be posted on CANVAS
› 25% Assignment:
– Individual assignment- written essay- choose between 3 topics.
– Not possible to get good marks if you leave this essay to last 2 weeks semester. – Read David Godden’s Literary Abominations and Other Crimes on CANVAS
– Due date: Fri May 27 11.59 pm
› 55% Final examination (2 hours), formal exam period. – Scenario based- Short answer questions
Essay assignment topic 1
› Choose an agricultural industry in Australia (e.g. dairy, beef, wheat etc), and examine the risks along the supply chain (e.g. risks facing farmers, processors, retailers). In your opinion, which level of the supply chain is exposed to the greatest level of risk? Who do you think is best placed to deal with risk in the supply chain and why? Support your reasoning with economic concepts and empirical evidence. (Length approx 1500 words).
› 1. Need to consider all sources of risk (see this lecture content).
› 2. Need to consider ways that are available to deal with risk.
Essay assignment topic 2
› “A grower representative group is urging political parties to address the market failure that exists in the fledgling Australian multi-peril crop insurance (MPCI) market.
› ThecallispromptedbyaGrainGrowersMulti-PerilCropInsurancereport,whichrevealedhowtheMPCI market is plagued by providers exiting the market and a lack of products.
› GrainGrowersiscallingonpoliticalpartiestopushforatemporary150%taxincentiveonMPCIand weather insurance premiums for five years, to reduce the cost of insurance to growers and to encourage advisers and accountants to support their grower clients to consider the appropriateness of MPCI to
improve their climate resilience and risk management, Grain Central reported”.
› MPCI market plagued by market failure – report 26 April 2019. Insurance Business Aust.
› https://www.insurancebusinessmag.com/au/news/breaking-news/mpci-market-plagued-by-market-failure– report-165732.aspx
› Using economic theory and empirical evidence critically evaluate the claim for a 150% tax incentive by GrainGrowers.
› 1. Need to consider the market failure (theory).
› 2. Need to consider merit of tax incentive as solution.
› 3. Need to consider empirical evidence.
› (Length approx 1500 words).
Essay topic 3
› Students may construct their own essay question in a topic of interest associated with agricultural risk. Your topic however must be approved. In order to obtain approval you must submit your question via email to:
› The question needs to be submitted before the end of Week 5 (Fri March 25) for approval.
› This is a hard deadline and no essay questions submitted after this will be accepted.
› To get good marks:
› 1. Answer the question (marks won’t be awarded for redundant writing)
› 2. Write concisely and precisely in academic style
› 3. Need to read widely
› 4. Start soon to allow plenty of thinking time
› 5. Allow time for a couple of edits. ›
Hardaker, J.B., Huirne, R.B.M., Anderson, J.R. and G. Lien (1997) Coping with Risk in Agriculture, CAB International.
Dixit, R. and R. Pindyck (1994) Investment Under Uncertainty, Princeton University Press. Moss, C.B. (2010) Risk, Uncertainty and the Agricultural Firm, World Scientific.
Randall, A. (2011) Risk and precaution. Cambridge University Press, UK. Electronic resource.
Rasmussen, S. (2011) Optimisation of Production Under Uncertainty. The State- Contingent Approach, Springer
Introduction
Incorporating attitudes to risk (I) and (II)
No tutorial
Decision analysis
Quantifying uncertainty (I)
Quantifying uncertainty (II)
Quantifying uncertainty from data
EV analysis; stochastic dominance
Mid semester exam
Introduction to real options
Real options
Tools for mitigating risk
Drought policy
No tutorial
Disability Services Support
You may not think of yourself as having a disability but the definition under the Disability Discrimination Act (1992) is broad and includes temporary or chronic medical conditions, physical or sensory disabilities, psychological conditions and learning disabilities.
Some types of disabilities we see include:
Anxiety // Arthritis // Asthma // Autism // ADHD Bipolar disorder // Broken bones // Cancer Cerebral palsy // Chronic fatigue syndrome
Crohn’s disease // Cystic fibrosis // Depression Diabetes // Dyslexia // Epilepsy // Hearing impairment // Learning disability // Mobility
impairment // Multiple sclerosis // Post-traumatic stress // Schizophrenia // Vision impairment
Students needing assistance are advised to register with Disability Services as early as possible. Please contact us or review our website to find out more.
Disability Services Office sydney.edu.au/disability 02 8627 8422
Uncertainty and economics
› Economics is a study of choices
– Production and supply decisions (we commonly assume) are based on profit
maximisation choices
– Consumer demand is based on choices made according to preferences and budget constraints
› Each choice might assume certain aspects of the decision problem are constant:
– Prices of inputs and outputs, production technology, and institutions
– The preference relationship
– The reality is that many of these things aren’t constant, and there can be additional complications
Uncertainty and economics
› The decision problem may be influenced by uncertain external factors: – Institutional (property rights) arrangements
– Governmental interventions
– Production conditions
– Market conditions
– Human factors
– Financial environment
Uncertainty and the agricultural firm
› We know that agricultural firms (farmers) are possibly more strongly influenced by these uncertainties than are other industries/types of economic activity
– Weather, for instance, is a factor which has limited impact on manufacturing firms
› Depending on the structure of the firm (farm), we may be able to manage some of these uncertainties
– Technology and production choice are key primary tools
– Financial markets (insurance, market-contracting, etc.) are secondary tools
Uncertainty and the agricultural firm
› This unit is concerned with developing these ideas into a concept of managing various types of uncertainties at the level of the firm
– In particular, at the level of the agricultural firm
› We will examine the uncertainties farms face and associated methods of quantification, methods of decision-making under uncertainty, and the tools available to farmers to manage uncertainties
What is uncertainty and risk?
› Uncertainty simply means being in an uncertain state
– Uncertain means we don’t have sufficient information to precisely determine
the outcome
› Some define risk as being uncertainty with consequences
– We don’t know what’s going to happen for sure, but the uncertainty will realise itself in a way that matters to us
– This could be good or bad
Knightian uncertainty and risk
› Risk as imperfect knowledge, where the probability of each possible outcome is known (Knight, 1933)
– e.g. Heads/Tails on an unbalanced coin with 40:60 likelihood, respectively › Uncertainty as imperfect knowledge, where the probability of each
possible outcome is unknown (Knight, 1933)
– e.g. Heads/Tails on a coin, but we can’t even put a number on the likelihood of
each occurrence
– Is it 40:60?
– Is it 30:70?
– Is it 50:50?
– We have no idea
– All we know is that ‘Heads’ and ‘Tails’ are possible outcomes
Confucian uncertainty and risk
› What we comprehend, relative to what there is to know:
– Known-Known: Deterministic – What we know with certainty – What we know exists and also understand
– Known-Unknown: Uncertain, but we know something about the system – What we know exists, but don’t fully understand
– Unknown-Known: Deterministic or uncertain, but we don’t know that we know
– Things that exist and have an impact on our lives, but we don’t fully comprehend (or we deny) their importance/existence
– Unknown-Unknown: No idea on either aspect – Definition of surprise
– By definition, we do not know they exist, let alone have the ability to foresee their consequences
Randallian uncertainty and risk
› Chance (unknowns) arises from three possible sources (Randall, 2011):
1) Outcomes generated by a random process
2) Outcomes generated by a system we don’t understand
3) Outcomes generated by a system that is complex and non- stationary
Systematic risk
› Systematic risks are uncertainties that affect aggregate outcomes: – For example, interest rates, aggregate demand/prices
– Also called un-diversifiable risk or market risk or aggregate risk
› Un-systematic risk captures the types of uncertainties particular to a commodity or firm/farm
– For example, risks particular to the cattle industry
– Also called specific risk or diversifiable risk or idiosyncratic risk – This is where we can target risk management
Australian Farm Institute (M.Keogh 2017)
Volatility (Australian Farm Institute (Keogh 2017))
Comparative relative volatility index of Australian agricultural sectors (2001-16) AFI 2019
Volatility (Australian Farm Institute (Keogh 2017))
Better risk management key for industry future: AFI’s Mick Keogh
› Feature of Australian agricultural policy for first 3⁄4 20th century was focus on reduction of risk for producers mostly via supporting agricultural prices and incomes(eg Dairy market pricing & regulation, buffer fund scheme – Reserve Price Scheme for wool industry, AWB single desk seller, wheat price stabilisation scheme-pooling of domestic and export prices)
› Since 1980s, slow dismantling of all of these schemes – farmers now manage own risks independently.
Sources of firm/farm risk
(1) Production and technical risk
– Biological processes may be impacted by weather, pests, disease
– When committing to a production decision, we don’t know exactly how much rain to expect – we may lose the investment we make in planting the crop (time, fertiliser, seed, etc.), if the rainfall level is too low
– Equipment (technical) may fail to perform at crucial time
– For example, the harvester fails and the crop remains unharvested – increased risk of quality downgrade
Mean annual temperature anomalies in Queensland, Australia. (Source: BOM in Hertzler et. al. 2006)
Weather variations-production uncertainty
Source: ABC Net – http://www.abc.net.au/news/2015-11-24/dilema-of-drought-as-el-nino-challenges-farmers-to-innovate/6930412
CSIRO map (Source: Hochman et al 2017)
Source:https://glamadelaide.com.au/heres-the-bushfire-affected-wineries-who-need-your-support/
Water crises: St Africa & MDB
Part of the problem? Over-allocation to water to agriculture
Photo: DAN PELED/EPA-EFE
NGS stock photo by
Photo:Facebook:
Source: abc.net.au
Source of firm/farm risk
(2) Marketing risk – prices and costs
– Supply of product is affected by production conditions and other farmers’ decisions – prices may drop at time of harvest
– Demand for product is influenced by standard demand factors, which may move unfavourably prior to, or at, sales time
– Costs of production tend to be less variable than output prices, but prices of key inputs (fuel, oil, fertiliser) can experience price spikes
– “cost price squeeze” – farm terms of trade
Australian Farm Institute
Agricultural market characteristics-implication for prices
› Tendency to be characterised by short-term unresponsiveness of supply and demand-this implies that both demand and supply curves will be steep.
› Why? On the demand side: lack of substitutes, basic staples, habit persistence in consumption patterns. On supply side: biological lags in production.
› Inelastic demand and supply in the short run implies price responses that clear the market will be larger than if elasticities were themselves large (highly responsive demand and supply).
Implications- agricultural price volatility
New supply
Original supply
Small supply shock and
relatively elastic demand
Price increase due to supply shock
Implications- agricultural price volatility
New supply
Original supply
Small supply shock can have large effect on price with ineleastic demand
Price increase due to supply shock
SR price behaviour: demand & supply (Source: Timmer(2008)
› Supply Function:
› Demand function:
D =aPsr P lr t ttdt−nd
S =bPsrP lr t ttst−ns
› Elasticities are measures of responsiveness of market participants (movements along the supply and demand functions) – these measure quantity and price responsive relationships
› Time dependent demand shifters captured in at
› Time dependent supply shifters captured in bt
SR price behaviour: demand & supply (Source: Timmer(2008)
› Pt = equilibrium market price during time t;
› Pt-n = market price during some previous time period t-n, › srd and srs, short-run demand and supply elasticities
› lrd and lrs long-run demand and supply elasticities
› Taking logs and in SR equilibrium (Dt=St):
loga +sr logP+lr logP =logb +sr logP+lr logP
t d t d t−n t s t s t−n
› Solving for Pt:
logP=(logb−loga)/(sr−sr)+logP (lr−lr)/(sr−sr) t t t d s t−n s d d s
SR price behaviour: demand & supply (Source: Timmer(2008)
What “causes changes in dlogPt?
– SR = srd-srs – LR= lrs-lrd
SR < 0 and LR > 0
dlogbt =logbt −logbt−1 dlogat =logat −logat−1
dogP = log P − log P ) t−n t−n t−n+1
Key drivers of the percentage change in price
› 1. relative size of changes in at to bt (supply and demand shifters)
› 2. relative size of short run demand and supply elasticities (srd, srs)
› 3. relative size of long run demand and supply elasticities (lrd, lrs)
› 4. magnitude of the past price change
› Assume the following numerical values: srd = -0.1, srs = 0.05, lrd = -0.3, lrs = 0.5, then SR= -0.15 and LR= 0.80, assume demand shifts 3% and supply shifts 1.5%. What if the change in past prices was -10%?
dlogPt =(1.5%−3%)/(−0.15)+(−10%)(0.8)/(−0.15) = 10% + 53% = 63%
Supply shifters (Source: Timmer)
› 1. Area expansion
› (i)Irrigationandcostofwater
› (ii)Deforestationandenvironmentalcosts
› (iii)“Benign”areaexpansioninAfricaandLatinAmerica?
› 2. Yield growth
› (i)Availabilityandcostsofinputs
› (a)Fertilizercosts
› (b)Energycosts
› (c)Sustainabilityissues
› (ii)SeedtechnologyandtheGMOdebate
› (iii)Managementimprovements/farmerknowledge
› 3. Variability
› (i)Weather
› (ii)Climatechange
Demand shifters (Timmer)
› 1. Population (driven by demographic transition, fertility, mortality, famine)
› 2. Income growth (driven by economic policy, trade, technology, governance)
› (i) Direct consumption
› (ii) Indirect consumption through livestock feeding or industrial utilization
› 3. Income distribution (driven by globalization, food prices, agricultural growth,
› structural transformation)
› 4. Biofuel demands (driven by political mandates and the price of petroleum)
› (i) Direct demand for maize and vegetable oils
› (ii) Ripple effects on other commodities
› 5. US dollar depreciation (most commodities on world markets are priced in dollars)
Demand shifters (Timmer)
› 6. Food prices (endogenous, driven by supply/demand balance and technical
› change; impact felt through the demand elasticities)
› 7. Private stockholding
› (i) Commercial (driven by price expectations and supply of storage)
› (ii) Household (driven by price panics and hoarding) Public stockholding (driven by buffer stock policy)
› (i) Trade policy
› (ii) Procurement policy
› 9. Financial speculation
› (i) Futures/options markets and “sophisticated” speculators
› (ii) Role of commodity index funds available to general investors
Commodity prices – stylised facts
› Supply shocks get magnified and distorted by inelastic and non-linear demand functions
› Stylised facts: commodity prices are highly autocorrelated, as well as displaying skewness, kurtosis and variability.
› Prices are signals to producers and consumers, when is variability “excessive”?
› Elasticities of supply and demand are critical for thinking about implications of policies
FAO Food Price Indices (1990-2016)
250.0 230.0 210.0 190.0 170.0 150.0 130.0 110.0
90.0 70.0 50.0
Food Price Index
Meat Price Index
Dairy Price Index
Cereals Price Index
6/1990 11/1990 4/1991 9/1991 2/1992 7/1992 12/1992 5/1993 10/1993 3/1994 8/1994 1/1995 6/1995 11/1995 4/1996 9/1996 2/1997 7/1997 12/1997 5/1998 10/1998 3/1999 8/1999 1/2000 6/2000 11/2000 4/2001 9/2001 2/2002 7/2002 12/2002 5/2003 10/2003 3/2004 8/2004 1/2005 6/2005 11/2005 4/2006 9/2006 2/2007 7/2007 12/2007 5/2008 10/2008 3/2009 8/2009 1/2010 6/2010 11/2010 4/2011 9/2011 2/2012 7/2012 12/2012 5/2013 10/2013 3/2014 8/2014 1/2015 6/2015 11/2015 4/2016
Food Price Spike (Timmer 2008)
Photograph: /EPA
Measures of volatility
› Various volatility measurements used in the literature on price volatility.
› Simple measures focus on total variability :
› 1.The standard deviation of prices or of logarithmic prices
› 2. The coefficient of variation -the standard deviation as a percentage of the sample mean (not dependent on the unit of measurement).
› 3. The standard deviation of the first difference in the logarithmic prices
› 4. De-trended price series can be used to compute volatility measures
› 3 and 4 require a model to be specified to approximate the nature of the underlying trend. Hence, these volatility measures necessarily depend on the choice of the de-trending.
Other measures reflect the variation around some trend – removal of trend movements in the volatility
ARCH and GARCH
› 5. Directly estimate a volatility model. For example, estimate a GARCH (Generalized Autoregressive Conditional Heteroscedasticity).
› Estimate the conditional variance of innovation from the auto-regressive process followed by a time series.
Data issues; nominal or real prices?
– real prices need to be deflated (issues- may introduces questions in the measure of volatility)
– No consensus on deflators
– Frequency – monthly or annual?
– Moving window? E.g. compute volatility of monthly data with a twelve month moving average
Coefficient of variation (CV)
›𝐶𝑉 = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 =
Corrected coefficient of variation (CCV)
› Corrected coefficient of variation (CCV) of level of prices using a
linear trend
程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com